BGH

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stringr)
install.packages("plotly")

## 
## The downloaded binary packages are in
##  /var/folders/fz/vk3jh7714xvg6zgxgtntpp1c0000gn/T//RtmpEv2fNn/downloaded_packages

install.packages("countrycode")

## 
## The downloaded binary packages are in
##  /var/folders/fz/vk3jh7714xvg6zgxgtntpp1c0000gn/T//RtmpEv2fNn/downloaded_packages

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
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## The following object is masked from 'package:graphics':
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library(countrycode)

# Load data
mortality <- read_csv("Maternal mortality ratio.csv")

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

## Rows: 185 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): name, slug, ranking
## dbl (2): 000 live births, date_of_information
## num (1): deaths/100
## lgl (1): region
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

vulnerability <- read_csv("vulnerability.csv")

## Rows: 192 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): ISO3, Name
## dbl (28): 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Clean mortality data
names(mortality) <- str_trim(names(mortality))
mortality <- mortality %>%
  rename(Country = name, Mortality = `deaths/100`) %>%
  mutate(Mortality = as.numeric(gsub(",", "", Mortality)))

# Prepare vulnerability data (2022 column)
vulnerability <- vulnerability %>%
  rename(Country = Name) %>%
  select(Country, Vulnerability = `2022`)

# Merge datasets
data <- inner_join(mortality, vulnerability, by = "Country")

# Add ISO3 codes for Plotly
data$ISO3 <- countrycode(data$Country, "country.name", "iso3c")

# Create choropleth map
fig <- plot_ly(data = data,
               type = 'choropleth',
               locations = ~ISO3,
               z = ~Mortality,
               text = ~paste(Country,
                             "<br>Mortality:", round(Mortality, 1),
                             "<br>Vulnerability:", round(Vulnerability, 3)),
               colorscale = 'Reds',
               marker = list(line = list(color = 'gray', width = 0.5)),
               colorbar = list(
                 title = "Maternal Mortality",
                 len = 0.5,
                 thickness = 10,
                 x = 1.05
               ))
fig <- plot_ly(data = data,
               type = 'choropleth',
               locations = ~ISO3,
               z = ~Mortality,
               text = ~paste(Country,
                             "<br>Mortality:", round(Mortality, 1),
                             "<br>Vulnerability:", round(Vulnerability, 3)),
               colorscale = 'Reds',
               marker = list(line = list(color = 'gray', width = 0.5)),
               colorbar = list(
                 title = "Maternal Mortality",
                 len = 0.5,          # Reduce vertical size
                 thickness = 10,     # Make it slimmer
                 x = 1.05            # Slightly offset to the right
               ))

fig <- fig %>%
  layout(
    title = 'Maternal Mortality Ratio & Climate Vulnerability (2022)',
    geo = list(
      showframe = FALSE,
      showcoastlines = FALSE,
      projection = list(type = 'natural earth'),
      bgcolor = 'white'
    )
  )
fig

library(htmlwidgets)
saveWidget(fig, "maternal_mortality_map.html", selfcontained = TRUE)